Using Elasticsearch and Kibana to analyze home energy consumption data from an IoT sensor network in Phoenix Arizona. The primary goal is HVAC optimization in a climate where summers exceed 115F.
Data pipeline:
- IoT sensors publish to MQTT
- Logstash ingests from MQTT broker
- Elasticsearch stores temperature and energy readings
- Kibana dashboards for visualization and alerting
Most useful Kibana features:
- Anomaly detection ML job that flags unusual energy spikes. Caught a failing HVAC capacitor before it killed the compressor. Energy draw was 15% higher than the learned baseline.
- Lens dashboard comparing energy consumption vs outdoor temperature with trendline. Clear inflection point at 105F where costs accelerate.
- Timelion for overlaying current day energy use on 7-day average.
The automation system using this data saves 15-18% on summer electricity bills through optimized pre-cooling schedules.
Anyone else using Elastic for IoT or energy data analysis? Curious what index strategies others use for high-frequency sensor data.